


import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      Adafactor,
      AdamW,
      get_constant_schedule,
      get_constant_schedule_with_warmup,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_inverse_sqrt_schedule,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=10   ):
   __a					=  []
   for _ in range(lowerCAmelCase__   ):
      lrs.append(scheduler.get_lr()[0]   )
      scheduler.step()
   return lrs




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=10   ):
   __a					=  []
   for step in range(lowerCAmelCase__   ):
      lrs.append(scheduler.get_lr()[0]   )
      scheduler.step()
      if step == num_steps // 2:
         with tempfile.TemporaryDirectory() as tmpdirname:
            __a					=  os.path.join(lowerCAmelCase__		,				"""schedule.bin"""   )
            torch.save(scheduler.state_dict()		,				lowerCAmelCase__   )

            __a					=  torch.load(lowerCAmelCase__   )
            scheduler.load_state_dict(lowerCAmelCase__   )
   return lrs


@require_torch
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        self.assertEqual(len(__A    )  ,       len(__A    )    )
        for a, b in zip(__A  ,       __A    ):
           self.assertAlmostEqual(__A  ,       __A  ,       delta=__A    )


     def 	snake_case_  (	self    ):
        __a					=  torch.tensor([0.1, -0.2, -0.1]  ,       requires_grad=__A    )
        __a					=  torch.tensor([0.4, 0.2, -0.5]    )
        __a					=  nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        __a					=  AdamW(params=[w]  ,       lr=2E-1  ,       weight_decay=0.0    )
        for _ in range(100    ):
           __a					=  criterion(__A  ,       __A    )
           loss.backward()
           optimizer.step()
           w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
           w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()  ,       [0.4, 0.2, -0.5]  ,       tol=1E-2    )


     def 	snake_case_  (	self    ):
        __a					=  torch.tensor([0.1, -0.2, -0.1]  ,       requires_grad=__A    )
        __a					=  torch.tensor([0.4, 0.2, -0.5]    )
        __a					=  nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        __a					=  Adafactor(
            params=[w]  ,       lr=1E-2  ,       eps=(1E-30, 1E-3)  ,       clip_threshold=1.0  ,       decay_rate=-0.8  ,       betaa=__A  ,       weight_decay=0.0  ,       relative_step=__A  ,       scale_parameter=__A  ,       warmup_init=__A  ,       )
        for _ in range(1000    ):
           __a					=  criterion(__A  ,       __A    )
           loss.backward()
           optimizer.step()
           w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
           w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()  ,       [0.4, 0.2, -0.5]  ,       tol=1E-2    )







@require_torch
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      nn.Linear(50 ,					50							) if is_torch_available() else None
     _lowerCamelCase   =      AdamW(m.parameters() ,					lr=10.0							) if is_torch_available() else None
     _lowerCamelCase   =      10


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A=None    ):
        self.assertEqual(len(__A    )  ,       len(__A    )    )
        for a, b in zip(__A  ,       __A    ):
           self.assertAlmostEqual(__A  ,       __A  ,       delta=__A  ,       msg=__A    )


     def 	snake_case_  (	self    ):
        __a					=  {"""num_warmup_steps""": 2, """num_training_steps""": 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        __a					=  {
            get_constant_schedule: ({}, [10.0] * self.num_steps),
            get_constant_schedule_with_warmup: (
                {"""num_warmup_steps""": 4},
                [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
            ),
            get_linear_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
            ),
            get_cosine_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
            ),
            get_cosine_with_hard_restarts_schedule_with_warmup: (
                {**common_kwargs, """num_cycles""": 2},
                [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
            ),
            get_polynomial_decay_schedule_with_warmup: (
                {**common_kwargs, """power""": 2.0, """lr_end""": 1E-7},
                [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
            ),
            get_inverse_sqrt_schedule: (
                {"""num_warmup_steps""": 2},
                [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
            ),
        }

        for scheduler_func, data in scheds.items():
           __a	,    __a					=  data

           __a					=  scheduler_func(self.optimizer  ,       **__A    )
           self.assertEqual(len([scheduler.get_lr()[0]]    )  ,       1    )
           __a					=  unwrap_schedule(__A  ,       self.num_steps    )
           self.assertListAlmostEqual(
               __A  ,       __A  ,       tol=1E-2  ,       msg=f'''failed for {scheduler_func} in normal scheduler'''  ,       )

           __a					=  scheduler_func(self.optimizer  ,       **__A    )
           if scheduler_func.__name__ != "get_constant_schedule":
              LambdaScheduleWrapper.wrap_scheduler(__A    )  # wrap to test picklability of the schedule
           __a					=  unwrap_and_save_reload_schedule(__A  ,       self.num_steps    )
           self.assertListEqual(__A  ,       __A  ,       msg=f'''failed for {scheduler_func} in save and reload'''    )







class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        __a					=  fn


     def __call__(	self  ,       *__A  ,       **__A    ):
        return self.fn(*__A  ,       **__A    )


     @classmethod
     def 	snake_case_  (	self  ,       __A    ):
        __a					=  list(map(self  ,       scheduler.lr_lambdas    )    )







__UpperCamelCase     		=							2_5_6
# Modulus to hash a string
__UpperCamelCase     		=							1_0_0_0_0_0_3
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		bool:
			UpperCamelCase__       =							len(_A	)
			UpperCamelCase__       =							len(_A	)
			if p_len > t_len:
						return False

			UpperCamelCase__       =							0
			UpperCamelCase__       =							0
			UpperCamelCase__       =							1

			# Calculating the hash of pattern and substring of text
			for i in range(_A	):
						UpperCamelCase__       =							(ord(pattern[i]	) + p_hash * alphabet_size) % modulus
						UpperCamelCase__       =							(ord(text[i]	) + text_hash * alphabet_size) % modulus
						if i == p_len - 1:
									continue
						UpperCamelCase__       =							(modulus_power * alphabet_size) % modulus

			for i in range(0					,  t_len - p_len + 1	):
						if text_hash == p_hash and text[i : i + p_len] == pattern:
									return True
						if i == t_len - p_len:
									continue
						# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
						UpperCamelCase__       =							(
						    (text_hash - ord(text[i]	) * modulus_power) * alphabet_size
						    + ord(text[i + p_len]	)
						) % modulus
			return False
def 							UpperCamelCase_(					)->		None:
			UpperCamelCase__       =							"abc1abc12"
			UpperCamelCase__       =							"alskfjaldsabc1abc1abc12k23adsfabcabc"
			UpperCamelCase__       =							"alskfjaldsk23adsfabcabc"
			assert rabin_karp(_A					,  _A	) and not rabin_karp(_A					,  _A	)

			# Test 2)
			UpperCamelCase__       =							"ABABX"
			UpperCamelCase__       =							"ABABZABABYABABX"
			assert rabin_karp(_A					,  _A	)

			# Test 3)
			UpperCamelCase__       =							"AAAB"
			UpperCamelCase__       =							"ABAAAAAB"
			assert rabin_karp(_A					,  _A	)

			# Test 4)
			UpperCamelCase__       =							"abcdabcy"
			UpperCamelCase__       =							"abcxabcdabxabcdabcdabcy"
			assert rabin_karp(_A					,  _A	)

			# Test 5)
			UpperCamelCase__       =							"Lü"
			UpperCamelCase__       =							"Lüsai"
			assert rabin_karp(_A					,  _A	)
			UpperCamelCase__       =							"Lue"
			assert not rabin_karp(_A					,  _A	)
			print("Success."	)


if __name__ == "__main__":
						test_rabin_karp()


